Locate and count known objects in images. Turn examples from your environment into a detection task your domain experts and engineers can evaluate together.
A business prepares kits containing several items. A detection model could locate visible items in a photo so a reviewer can check the contents against the expected kit.
Illustrative scenario, not a customer result.A detection model predicts an object class and its location. This makes it a useful starting point for counting items in a photo, checking a prepared kit, or identifying products on a shelf. Define the scene and the classes before selecting a model.
Decide whether a box should include only the visible area or an estimated full object. Keep that convention consistent across annotators. Repeated images of the same scene can make validation look easier than deployment, so hold out genuinely different scenes.
Precision and recall help describe detection quality, but the application may care about a count, a missing item, or a location. Review errors at that level too. A confidence threshold changes the balance between missed objects and unnecessary alerts.
Classification assigns a category to an image. Detection identifies individual objects and their positions, often with bounding boxes. Choose detection when the location, presence, or number of individual objects matters to the decision.